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In the great majority of urban models based on Cellular Automata (CA), the concept of proximity is assumed to reflect two fundamental sources of spatial interaction: (1) the accessibility of places and (2) the distance ˵as the crow flies″. While the geographical space defined by the latter clearly has an Euclidean representation, the former, based on the accessibility, does not admit such a regular representation. Very little operational efforts have been undertaken in CA-based urban modelling to investigate and provide a more coherent and cogent treatment of such irregular geometries, which indeed are essential and crucial feature of urban geography. In this paper, we suggest an operational approach – entirely based on cellular automata techniques – to model the complex topology of proximities arising from urban geography, and to entangle such proximity topology with a CA model of spatial interactions. [ABSTRACT FROM AUTHOR]

We present a comparative study of seven evolutionary algorithms (Generational Genetic, Elitist Genetic, Steady State Genetic, (μ/ρ, λ) Evolution Strategy, (μ/ρ + λ) Evolution Strategy, generational and elitist Covariance Matrix Adaptation) for automatic calibration of a constrained cellular automaton (CCA), whose performance are assessed in terms of two fitness metrics (based on Kappa statistics and Lee-Salee Index). Two variations of the CCA (one with 14 and one 27 parameters) were tested jointly with different number of time steps targeted by the calibration procedures. Besides offering some methodological suggestions for this kind of comparative analysis, the findings provide useful hints on the calibration algorithms to be expected to perform better in the application of cellular automata of sort for the simulation of land-use dynamics. [ABSTRACT FROM AUTHOR]

Urban planning is confronted with multifaceted complexities, related to the complex nature of phenomena, dynamics and processes it has to deal with. We argue that good tools for planning must be informed by these complexities, and therefore must have specific characteristics, in terms of modularity, flexibility, user-friendliness, generality, adaptability, computational efficiency and cost-effectiveness. In this chapter we present and try to make the case for a multi-agent geosimulation infrastructure framework called MAGI, showing how it delivers as such a tool for planning. The modelling and simulation infrastructure MAGI possesses characteristics, features and computational strategies particularly relevant for strongly geo-spatially oriented agent-based simulations. The infrastructure is composed of a development environment for building and executing simulation models, and a class library based on open source components. Differently from most of the existing tools for geosimulation, both raster and vector representation of simulated entities are allowed and managed with efficiency. This is obtained through the integration of a geometry engine implementing a core set of operations on spatial data through robust geometric algorithms, and an efficient spatial indexing strategy for moving agents. [ABSTRACT FROM AUTHOR]

This paper presents an integrated GIS and cellular automata (CA) modelling and simulation environment. We hold that several concurrent facts offer the necessary critical mass to foster the development of robust and scalable general-purpose CA-modelling tools that go beyond demonstrative proof-of-concept status, allowing thus fully operational modelling and simulation of real-world territorial systems. Among these facts, we can mention the entering of the CA-based geosimulation modelling in its age of maturity, the growing computational capabilities of nowadays computers, the consolidation of general-purpose as well as specialised GIS-based geo-analysis methodologies and their availability as open-source libraries and applications. The environment hereby described is an example of a way to obtain such robust and general-purpose CA-modelling tool-box. Its main characteristics are (1) tight interoperability between GIS and CAs, (2) a sufficiently generalised and flexible underlying CA meta-model allowing for the implementation of a variety of types of CA models, (3) computationally efficiency and (4) user-friendliness. We furthermore present two test case applications to illustrate a standard operational modelling workflow within this simulation environment and to briefly illustrate its look-and-feel. [ABSTRACT FROM AUTHOR]

In this paper we present the general-purpose simulation infrastructure MAGI, with features and computational strategies particularly relevant for strongly geo-spatially oriented simulations. Its main characteristics are (1) a comprehensive approach to geosimulation modelling, with a flexible underlying meta-model formally generalising a variety of types of models, both from the cellular automata and from the agent-based family of models, (2) tight interoperability between GIS and the modelling environment, (3) computationally efficiency and (4) user-friendliness. Both raster and vector representation of simulated entities are allowed and managed with efficiency, which is obtained through the integration of a geometry engine implementing a core set of operations on spatial data through robust geometric algorithms, and an efficient spatial indexing strategy for moving agents. We furthermore present three test-case applications to discuss its efficiency, to present a standard operational modelling workflow within the simulation environment and to briefly illustrate its look-and-feel. [ABSTRACT FROM AUTHOR]

The difficulty in dealing with urban systems' complexity and the related difficulty to analyse and forecast is twofold: one kind of difficulty lies in the complexity of the system itself, and the other is due to the actions of actors, which are "acts of freedom". In our contribution we would like to present a set of techniques and models, with respective software packages (MaGIA, The Time Machine, CAGE and GioCoMo), that have proven to be of great potential for enactment and management of communication, participatory, consensus-building and simulation processes. As such, our approach tries to cope with both aspects of complexity mentioned above. [ABSTRACT FROM AUTHOR]

In this paper we present characteristics, features and computational strategies used in MAGI (Multi-Agent Geosimulation Infrastructure) which are relevant for strongly geo-spatially oriented agent-based simulations. These characteristics make MAGI an effective modelling and simulation environment, in terms of generality, user friendliness, modelling flexibility, interoperability with GIS datasets and computational efficiency. The infrastructure is composed of a development environment for building and executing simulation models, and a class library based on open source components. Differently from most of the existing tools for geosimulation, both raster and vector representation of simulated entities are allowed and managed with efficiency. This is obtained through the integration of a geometry engine implementing a core set of operations on spatial data through robust geometric algorithms, and an efficient spatial indexing strategy for moving agents. [ABSTRACT FROM AUTHOR]

In this paper we present a methodological approach and a client-server software authoring environment for cooperative visual creation of story-outlines. The methodological approach adopted for story outlining facilitates the representation and visualisation of relevant aspects of stories in a way that is easy to use and understand, and enough general to admit a large number of types and genres of stories. Additional design objectives were set in order to support effective cooperative work and rapid story prototyping. [ABSTRACT FROM AUTHOR]

Sustainable tourism development at a destination requires an understanding of the many interrelated factors that influence risks and returns. For this reason, operators in tourism management and public policy makers are increasingly interested in effective tools for exploring the consequences of their decisions. With this purpose, this paper describes a multiagent system which enables evaluation of the impact of some possible interventions and policies at a destination by computer simulations and provides an insight into the functioning of the market. [ABSTRACT FROM AUTHOR]

This paper presents the general philosophy, features and few aspects related to the implementation and underlying technologies of MaGIA, an Internet-based multi-user system designed for a collective and collaborative construction of knowledge models represented as concept maps. The use of the system can cover a wide range of purposes, from a theoretical discussion and construction of formal models to a support for collective brain-storming. What makes MaGIA an interesting tool is the emphasis put on a collective construction of such maps. In fact, one of the distinctive features and objectives of the system is to offer an enabling tool for a multi-user, bottom-up construction, where users can intervene, freely contribute and extend concept maps and where collective and collaborative multi-user map construction can take place in asynchronous as well as in synchronous way. [ABSTRACT FROM AUTHOR]

Strategic planning can be schematised as a decision making process where, given a general outline of the desirable future, the decision makers need to choose a set of actions that should coherently lead a system (corporation, institution, city, region, etc.) toward that future. A more sophisticated case is when rather than only choosing actions, the decision maker also decides the allocation of available resources among different available actions. We show that in most cases the problem can be faced using a particular Decision Network with multiple objectives, in which actions are applied simultaneously and are modelled by variables representing the efforts spent on them. The main advantage of the proposed Simultaneous Decision Network is that it can be easily built by a panel of domain experts, under the assumption of the noisy-OR causal interaction. The problem of finding the best strategy in terms of resource allocation is formulated as a combinatorial optimisation, and solved through a multi-objective meta heuristic approach. [ABSTRACT FROM AUTHOR]

The knowledge-driven causal models, implementing some inferential techniques, can prove useful in the assessment of effects of actions in contexts with complex probabilistic chains. Such exploratory tools can thus help in "forevisioning" of future scenarios, but frequently the inverse analysis is required, that is to say, given a desirable future scenario, to discover the "best" set of actions. This paper explores a case of such "future-retrovisioning", coupling a causal model with a multi-objective genetic algorithm. We show how a genetic algorithm is able to solve the strategy-selection problem, assisting the decision-maker in choosing an adequate strategy within the possibilities offered by the decision space. The paper outlines the general framework underlying an effective knowledge-based decision support system engineered as a software tool. [ABSTRACT FROM AUTHOR]